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Hands-On Ensemble Learning with Python

By : Kyriakides, Margaritis

Hands-On Ensemble Learning with Python

By: Kyriakides, Margaritis

Overview of this book

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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Section 1: Introduction and Required Software Tools

Section 1: Introduction and Required Software Tools

A Machine Learning Refresher

A Machine Learning Refresher

Technical requirements

Learning from data

Supervised and unsupervised learning

Performance measures

Machine learning algorithms

Summary

Getting Started with Ensemble Learning

Getting Started with Ensemble Learning

Technical requirements

Bias, variance, and the trade-off

Ensemble learning

Difficulties in ensemble learning

Summary

Section 2: Non-Generative Methods

Section 2: Non-Generative Methods

Voting

Voting

Technical requirements

Hard and soft voting

Python implementation

Using scikit-learn

Summary

Stacking

Stacking

Technical requirements

Meta-learning

Deciding on an ensemble's composition

Python implementation

Summary

Section 3: Generative Methods

Section 3: Generative Methods

Bagging

Bagging

Technical requirements

Bootstrapping

Bagging

Python implementation

Using scikit-learn

Summary

Boosting

Boosting

Technical requirements

AdaBoost

Gradient boosting

Using scikit-learn

XGBoost

Summary

Random Forests

Random Forests

Technical requirements

Understanding random forest trees

Creating forests

Using scikit-learn

Summary

Section 4: Clustering

Section 4: Clustering

Clustering

Clustering

Technical requirements

Consensus clustering

Using OpenEnsembles

Summary

Section 5: Real World Applications

Section 5: Real World Applications

Classifying Fraudulent Transactions

Classifying Fraudulent Transactions

Technical requirements

Getting familiar with the dataset

Exploratory analysis

Voting

Stacking

Bagging

Boosting

Using random forests

Comparative analysis of ensembles

Summary

Predicting Bitcoin Prices

Predicting Bitcoin Prices

Technical requirements

Time series data

Voting

Stacking

Bagging

Boosting

Random forests

Summary

Evaluating Sentiment on Twitter

Evaluating Sentiment on Twitter

Technical requirements

Sentiment analysis tools

Getting Twitter data

Creating a model

Classifying tweets in real time

Summary

Recommending Movies with Keras

Recommending Movies with Keras

Technical requirements

Demystifying recommendation systems

Neural recommendation systems

Using Keras for movie recommendations

Summary

Clustering World Happiness

Clustering World Happiness

Technical requirements

Understanding the World Happiness Report

Creating the ensemble

Gaining insights

Summary

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Another Book You May Enjoy

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Summary

In this chapter, we presented one of the most powerful ensemble learning techniques, boosting. We presented two popular boosting algorithms, AdaBoost and gradient boosting. We presented custom implementations for both algorithms, as well as usage examples for the scikit-learn implementations. Furthermore, we briefly presented XGBoost, a library dedicated to regularized, distributed boosting. XGBoost was able to outperform all other methods and implementations on both regression as well as classification problems.

AdaBoost creates a number of base learners by employing weak learners (slightly better than random guessing). Each new base learner is trained on a weighted sample from the original train set. Weighted sampling from a dataset assigns a weight to each instance and then samples from the dataset, using the weights in order to calculate the probability that each instance...

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